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  • Book Overview & Buying Machine Learning For Dummies
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Machine Learning For Dummies

Machine Learning For Dummies

By : John Paul Mueller, Luca Massaron
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Machine Learning For Dummies

Machine Learning For Dummies

By: John Paul Mueller, Luca Massaron

Overview of this book

Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn’t be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. In the initial chapters, the book introduces you to the world of machine learning, artificial intelligence, big data, and will prepare you to use R and Python for machine learning tasks. Next, you’ll learn how to use math in machine learning and get started with linear models and neural networks. In the final chapters, you’ll process images and text, and discover packages and techniques to improve your machine learning models. By the end of this book, you’ll be able to understand and implement machine learning seamlessly.
Table of Contents (34 chapters)
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2
Part 1: Introducing How Machines Learn
6
Part 2: Preparing Your Learning Tools
12
Part 3: Getting Started with the Math Basics
17
Part 4: Learning from Smart and Big Data
24
Part 5: Applying Learning to Real Problems
28
Part 6: The Part of Tens
31
About the Author
32
Advertisement Page
33
Connect with Dummies
34
End User License Agreement

Testing Multiple Models

The no-free-lunch theorem should always be an inspiration for you, reminding you not to fall in love with certain learning approaches just because they brought interesting results in the past. As a good practice, test multiple models, starting with the basic ones — the models that have more bias than variance. You should always favor simple solutions over complex ones. You may discover that a simple solution performs better. For example, you may want to keep things simple and use a linear model instead of a more sophisticated, tree-based ensemble of models.

tip Representing the performance of different models using the same chart is helpful before choosing the best one to solve your problem. You can place models used to predict consumer behavior, such as a response to a commercial offer, in special gain charts and lift charts. These charts show how your model performs by partitioning its results into deciles or smaller parts. Because you may be interested only...

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Tech Concepts
36
Programming languages
73
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Machine Learning For Dummies
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